> Markdown version of [/jobs/ext/3845995-ai-governance-and-security-architect](https://www.wearedevelopers.com/jobs/ext/3845995-ai-governance-and-security-architect). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI Governance and Security Architect - **Company:** Mitchell Martin - **Location:** Kansas City, MO, United States - **Experience:** Expert - **Salary:** $131,040.0 - $178,880.0 - **Contract:** Contract - **Skills:** Training Data, Artificial Intelligence, Amazon Web Services, Microsoft Azure, Data Architecture, Information Engineering, Data Governance, Dataspaces, Data Systems, NoSQL, Cloud Services, SQL Databases, Google Cloud, Snowflake, Apache Spark, Usage Tracking, Apache Kafka, Data Management, Data Pipelines - **Published:** October 20, 2026 - **Apply:** https://www.dice.com/job-detail/3cca553d-22b6-4637-851d-2eb33b055c75 ## About the Role Bachelor's degree in a relevant field with at least 7 years of experience in data architecture or equivalent experience. Proven experience in designing data platforms supporting AI initiatives. Strong understanding of data governance and compliance in financial services. Experience collaborating with cross-functional teams for enterprise-grade data solutions. Proficiency with cloud data platforms and data integration patterns. Core Technologies AWS | Azure | Google Cloud Platform | SQL | NoSQL | Kafka | Spark | Snowflake ## Description Join our team in a contract role focused on designing secure, scalable AI data architectures in Kansas City, MO. This position involves strategic leadership in modernizing data ecosystems and aligning AI-ready platforms with business priorities. You will work with advanced data engineering and governance technologies to ensure reliable and compliant AI usage., Define and implement enterprise-wide AI data architecture strategies for secure and compliant AI adoption. Design data models, metadata frameworks, and pipelines to support AI model lifecycle. Collaborate with governance teams to enforce responsible AI data usage policies. Architect AI-ready data platforms to integrate various data sources. Optimize AI workloads on cloud platforms and manage data pipelines.